| title | company_name | location | via | description | searching keyword | Qualifications | Responsibilities | salary | schedule_type | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ethereum Blockchain Developer (Remote) | Ex Populus | Anywhere | Built In | Company Overview: Ex Populus is a cutting-edge... | block chain | 2-3 years of Software Development experience 1... | Design, maintain and deploy smart contracts fo... | NaN | Full-time |
| 1 | Blockchain Engineer | 21.co | New York, NY | Greenhouse | We are seeking a highly motivated and skilled ... | block chain | Bachelor's or Master's degree in Computer Scie... | As a Blockchain Engineer, you will be responsi... | NaN | Full-time |
| 2 | Blockchain Course Instructor | Blockchain Institute of Technology | Anywhere | Are you a blockchain, cryptocurrency, NFT, Met... | block chain | 3+ years of experience in blockchain, cryptocu... | Our expert technical team will provide the sup... | NaN | Contractor | |
| 3 | Python based - Blockchain developer to join ex... | Upwork | Anywhere | Upwork | Need someone to join our existing team to spee... | block chain | Candidates must be willing to sign, non-disclo... | Will discuss details with the selected candidates | 10–30 an hour | Contractor |
| 4 | Blockchain DevOps Engineer (Remote) | Telnyx | United States | Startup Jobs | About Telnyx At Telnyx, we’re architecting an... | block chain | You are a highly motivated and experienced Blo... | To build a best-in-class Filecoin (FIL) Mining... | NaN | Full-time |
Analyzing Job Trends in the Analytics Field: Insights and Tips for Job Seekers
1. Introduction
In recent years, the discipline of analytics has expanded significantly. There is a rising need for qualified experts who are able to interpret complicated data sets and provide conclusions to inform business decisions. As a result, the analytics job market is getting more and more competitive, and job searchers face a challenging environment of titles, requirements, and duties.
In this project, I examined employment trends in the data market using a dataset of pertinent job posts. I gave insight into the abilities and expertise needed for occupations using data by looking at the requirements and duties of various roles, as well as the number of opportunities available in various places.
2. Data
After cleaning and merging two datsets, the data table below shows a brief look at the data I used in this project.
The data was collected provided by DSAN in Georgetown University. The data includes the following columns: title, company name, location, salary, job description, and searching keyword. The searching keyword column is a column I added to the dataset. It includes the job title and the searching keyword I used to search for the job. The searching keyword column is used to group the data by job title.
3. Top 10 Companies and Cities for Data Job Postings
Fig-1 displays the top 10 cities and companies that are currently offering jobs. The list of the top ten companies includes those from diverse fields such as internet, retail, finance, and more. UPWORK is at the top of the list, offering the most sought-after jobs, perhaps because it specializes in connecting growing businesses with exceptional talent and agencies.
Most of the cities with the highest number of jobs are large cities like New York, Washington DC, and California. While large cities do offer more job opportunities, it does not necessarily imply an absolute advantage for data-related jobs. The large number of job openings also corresponds to a sizable resident population, which could result in intense competition for applicants.
4. Job Trends by State
Note
‘WFH’ means ‘Work From Home’
Fig-2 indicates that the trend of remote work continues to increase, especially in the data-related field. The 24-hour nature of cryptocurrency trading has resulted in a predominance of remote work options. However, even beyond the virtual currency industry, many companies offer work-from-home opportunities for their employees in the data-related field.
While WFH has become the norm, California remains a top workplace for data-related jobs. California’s Silicon Valley is known for its concentration of technology companies, making it a hub for job opportunities in this field. Additionally, California’s tech industry is well-established, providing excellent resources for individuals seeking to advance their careers in this field.
5. Qualifications and Responsibilities for Different Job Types
Note
‘Quals’ means ‘Qualifications’
‘Resps’ means ‘Responsibilities’
| Qualifications: | Responsibilities | |
|---|---|---|
| Big Data and Cloud Computing | Distributed Computing, Cloud distributed | Software development, Solution, Customer |
| Block Chain | Smart contract, Security | Development, Security, Solution, Product |
| Data Analyst | Business, Management, SQL, Bachelor degree | Report, Support, Management, Business, Develop |
| Data Scientist | Python, Machine learning, Analytic, Engineering | Model, Analytic, Bussiness, Support, Develop |
| Deep Learning | Deep learning, Machine Learning Product, Model, Solution, Customer | Model, Deep learning, Machine Learning |
| Natural Language Processing | Natural language, Language processing, Machine Learning | Nlp, Model, Research, Project |
| Neural Networks | Neural network, Deep learning, Pytorch, Tensorflow, Engineering | Design, Product, Neural network, Model, Research |
| Reinforcement Learning | Machine learning, Deep Learning, Model, Python, Algorithm, Reinforcement learning | Machine Learning, Research, Model, Solution, Product, Business |
| Time Series Analysis | Time series, Model, Python, Statistics, Machine Learning | Project, Model, Analytic, Business, Algorithm |
From Figure-3 and Table-1, while Big Data and Cloud Computing focuses on distributed computing and cloud infrastructure, Blockchain is concerned with smart contracts and security. Data Analysis involves generating reports and managing data, while Data Science requires the creation of models and analysis of data. Deep Learning and Natural Language Processing are more specific and require expertise in deep and machine learning or natural language, language processing, and machine learning, respectively.
Neural Networks, on the other hand, require knowledge in neural networks, deep learning, PyTorch, TensorFlow, and engineering, with a focus on designing products and managing projects. Reinforcement Learning requires knowledge of machine and deep learning, Python, algorithms, and reinforcement learning and requires research and product development. Time Series Analysis is concerned with managing projects and analyzing data, requiring knowledge of time series, Python, statistics, and machine learning.
6. Skills Needed for Data Jobs
Based on the information from Figure-4, Python, Java, SQL, and R are the most commonly requested skills for data-related jobs in the US. Cloud computing skills are also highly desired, with AWS being the most requested cloud platform followed by Azure and GCP. Other commonly requested skills include Spark, Tableau, Scala, Excel, and C. Docker, Kubernetes, and Linux are also frequently requested for their containerization and infrastructure management capabilities. Some skills like C++, QlikView, D3.js, Alation, Kaizen, Reno, DataHub, Looker, Swift, Grafana, and Julia do not seem to be as in-demand in the current job market for data-related roles. Even though GitHub is not in the left figure, it is still a very important tool for data-related jobs.
Note
Please Double Click the legend that you are interested in to show top five skills for each job type.
Python is a popular tool across the majority of data-related job categories, including big data and cloud computing, data scientists, deep learning, machine learning, natural language processing, neural networks, reinforcement learning, and time series analysis, according to the provided data. Additionally, Java is frequently sought, especially for big data and cloud computing roles. For data analyst roles, SQL, Tableau, and Excel are regularly requested, whereas R and SAS are more typically requested for data scientist roles.
Platforms for big data and cloud computing, blockchain, deep learning, machine learning, natural language processing, and reinforcement learning are all in high demand, with AWS being the most sought-after platform in all of these fields. Although less frequently than AWS, Azure and GCP are also commonly asked. Popular tools for big data and cloud computing tasks include Docker and Kubernetes.
7. Conclusion
It is essential for anybody looking for work in the analytics industry to be aware of the latest trends and industry standards. Python, Java, SQL, and R are the most often required talents for data-related employment in the US, according the data examined for this project. In addition to being in great demand, additional technologies including Spark, Tableau, Scala, Excel, and C are also frequently sought.
It’s also important to notice that WFH possibilities are widely available and that remote employment opportunities are growing more common. However, it is significant to balance the advantages of living in a big city with the potential difficulties in getting employment because there is tremendous job seekers for jobs in big cities like New York and California.
Additionally, the qualifications and responsibilities of various job categories are slightly different, thus job searchers should notice these variations in order to change their applications strategies. While data scientists need to be proficient in Python and R, data analysts need to be proficient in SQL, Tableau, and Excel. While professions in deep learning and natural language processing demand knowledge of both machine learning and natural language, big data and cloud computing positions demand for expertise in Java and cloud platforms like AWS.
Job searchers should concentrate on developing their abilities in the tools and technologies that are most in demand as well as staying current with industry developments.